災害に強い電子カルテ高知大学 医学部附属医学情報センター
畠山 豊、 片岡 浩巳
災害時の医療継続に向けて
• カルテ情報の保全• 複数の拠点でのクラウド化
• 国立大学病院間における医療情報システムデータのバックアップ対しの構築（平成25年度中に稼働開始）
• PHRの構築
• 震災直後における患者データ利用• 通信インフラの確保
前提どこで医療サービスを受けても同じ品質の履歴が生成・蓄積される
「どこでもMY病院」構想• 利用者向けのサービス
• PHR（Personal Health Record）• だれもがよりよい健康・医療サービスを選択し受けやすくするための電子的な記録および意思決定支援システム
PHRで検査データも統合される時代に
• 初期診療、急性期診療、慢性期診療の役割分担• 急性期は平均在院日数の短縮→複数の病院に受診
• 各検査室のデータの精度• 施設間差
• 基準値、臨床判断値の共有化
• 長期的精度管理• 時系列変化• 症例の蓄積→過去のデータは重要
検査値の精度管理が重要に
電子カルテ統合に向けて
どこでもMY病院構想
2010年～ 2014年にはサービス提供開始2014年にはサービス提供開始
・内閣官房、厚生労働省、経済産業省、総務省、国を挙げての大構想
個人の識別IDは？個人の識別IDは？
コードの標準化は？
通信プロトコールは？通信プロトコールは？
過去の検査データの標準化は？過去の検査データの標準化は？
検査データの標準化は？検査データの標準化は？
検査データ：莫大な予算を投入標準物質に対する、トレーサビリティ、コミュータビリティの確保
マイナンバー法は現在、医療で使えない
HL7ベースでほぼ可能
検査コードは問題だらけ
解決すべき難問
精度管理手法の分類
リアルタイムQC（実時間精度管理）
経日的精度管理（短期的精度管理）
経年的精度管理（長期的精度管理）
検量線の管理反応過程チェック検体情報チェック上下限範囲チェック項目間相関チェック前回値チェックX－Rs管理図
X-R管理図X-Rs-R管理図マルチルール管理累積和管理図双値法Youden図正常者平均法
標準物質による評価コントロールサーベィ技能試験基準値との比較評価正常者平均法
日本臨床検査自動化学会会誌Vol.38 Suppl.2より
・認証標準物質の測定・外部精度管理
年に数回など課題山積
検査値の施設間誤差に対する対策
SI 単位の定義
NMIJ 認 証 標 準 物 質純 度 の 測 定 方 法
基 準 測 定 方 法
JSCC など学会勧告法
社内標準測定操作法
日常検査法（試薬キット）
常 用 参 照 標 準 物 質
実 用 標 準 物 質
校正物質（キャリブレータ）
日 常 試 料
標準化された測定結果
校正値付け
（日本臨床検査標準協議会 ホームページより）
物 質
操 作 法トレーサビリティ
校正物質の標準化とトレーサビリティの確保
コミュータビリティの確保
高価
安価
外部精度管理によるバラツキの実態
• 比較的安定とされる乳酸脱水素酵素(LD)の事例
1999年 2007年
日本医師会 外部精度管理報告書より引用
ドライケミストリー→自施設の緊急検査など
臨床検査における精度保障の概要と課題
トレーサビリティの確保
コミュータビリティの確保
パッチワーク方式外部精度管理
内部精度管理
ヒトプール血清を用いた標準化活動地域施設への正確さを伝達
これらが完璧なら、長期時系列精度や共有基準範囲も保障されるはず
血液、生理、病理などの人為的判断による物は、もっと客観的な指標が必要か？
・年に数回しか確認できない・膨大な労力とお金・評価後の対処法の確立
（これが臨床側には大問題となる）
・短期ロットのつぎはぎ問題・異なるロットの並行測定運用を行っている？
電子カルテの３原則による束縛
• 真正性• 書換、消去・混同、改ざんを防止すること。
• 作成者の責任の所在を明確にすること。
• 見読性• 必要に応じ肉眼で見読可能な状態にできること。
• 直ちに書面に表示できること。
• 保存性• 法令に定める保存期間内、復元可能な状態で保存すること。
正規化補正は改ざんではない
HIS側にも標準化し
た検査結果の表示機能要か？
長期時の系列誤差を把握するには？AgeGLU
年月 年月
随時血糖と年齢の30年間の分布
高齢化・世代による分布の変化あり分布は上昇傾向であるが，
頻値は変化なし
mg/dl 歳
連続性が維持できていることの検証法
頻値変化なし
12
測定法変更に伴う長期時系列検査データの整備
乳酸脱水素酵素(LD)の30年間にわたる集団分布（年月毎に度数分布）
測定方法の変更
13
LD
年月
切り替え時同時測定した結果から換算係数を算出
測定法、分析装置変更時の手順（正確度のミスが発見されても）
14
測定法変更に伴う運用プロトコルの基本
新測定法
旧測定法
①項目コード新設
②同時測定期間
③補正係数の算出
④DWH側で換算
時間
検査値
実測値による換算係数の導出と，係数の管理
未来に向けてマスター記録を徹底すれば解決
15
検査部からのアナウンスの例
16
それでも、想定外は発生する
17
‐1.5
‐1
‐0.5
0
0.5
1
1.5
λ トランケーション平均
Jaffe法から酵素法に変更
CRE
λ
年月
CREの長期的精度の可視化
18
BOX‐COX変換べき乗値
開院時の患者層？
試薬のダイナミックレンジ？
標準物質の問題？内部精度管理の問題？
AKIの診断基準は？
• 48時間以内で血清クレアチニン値の変動• 0.3mg/dl以上上昇
• 基礎値の1.5倍上昇
• 尿流量の減少• 0.5ml/Kg以下（6時間）
Mehta,R.L., Kellum,J.A., Shah,S.V., et al.: Acute Kidney Injury Network: report of an initiative to improve outcomes in acute kidney injury. CritCare 2007;11:R31
この基準の根拠は，測定許容誤差を超える変動でも病院死や1年以内
の死亡の危険因子であったアウトカムによる
19
CREの事例
A大学 B大学
ただし、患者層を考慮せず、全データの分布図
検査値の連続性を保つためには
• 未来に向けての対策– 実測値による換算係数の導出
– 外部＋内部精度管理データで補正
– パッチワーク方式等の地域外部精度管理データを取り込む
• ヒト由来サンプルでなければうまく行かないため。
• 過去のデータに対する対策– データの分布を用いて変換
• これしかデータが残っていない
方法間、施設間変動の正規化
総変動
施設間の変動
他の病院に受診しても同じ基準で診断可能
病院１ 病院２ 病院３
患者群（準健康患者群）の分布
ただ集めただけのDWHでは多くの誤差を含んだデータとなる
正規化正規化
過去のデータには、補正するためのデータが残っていない。
誤差が大きすぎて使え
ない
誤差が大きすぎて使え
ない
測定法間の変動方法１ 方法２ 方法３
分布形の正規分布への変換
正規分布への変換
24
分布の偏り→べき乗変換で正規分布に
度数
a a a
x 3 xx2x3 x logx
x1 x0.5 x0.33 x0
p = 3.0 2.0 1.0 0.5 0.33 0.0
Box‐Cox変換によるべき乗係数の導出
正規分布 対数正規分布
べき乗
BOX‐COX変換の原理
25
1λ 1 , λ 0 log , λ 0 べき乗値
データ
Histogram of stdLD
stdLD
Freq
uenc
y
0.00 0.01 0.02 0.03 0.04
010
020
030
040
050
0
Histogram of LD
LD
Freq
uenc
y
0 100 200 300 400 500
010
020
030
040
0
各べき乗値における正規性
-3 -2 -1 0 1 2 3
050
100
150
200
lambda0
resu
lt
λ=‐0.78
分布
の幾
何平
均補
正標
準偏
差
λ：べき乗値
べき乗による正規分布への変換
26
Histogram of dat$LD
dat$LD
Freq
uenc
y
0 200 400 600 800 1000
050
010
0015
00
Histogram of powLD
powLD
Freq
uenc
y
0.00 0.01 0.02 0.03 0.04
020
040
060
080
010
00
BOX‐COX変換
Mean=0.016390^(1/‐0.78)=195
べき乗変換
逆変換
λ=‐0.78
トランケーションで外れ値排除
（LDの実測換算値が無い場合の事例）
年齢、性別、多次元、束縛変換対象データ抽出
BOX-COX変換
べき乗変換
トランケーション
平均値:a2
旧データ:d2 新データ:d1
年齢、性別、多次元、束縛変換対象データ抽出
BOX-COX変換
べき乗変換
トランケーション
平均値:a1
Mean±2SD除去 Mean±2SD除去補正:
d2*m1/m2
補正計数導出:m1/m2
旧法補正結果
適λ2 適λ1
べき乗逆変換:m1=pow(a2,1/λ2)
べき乗逆変換:m2=pow(a1,1/λ1)
母集団の分布から補正係数を推定新旧同時測定してない施設向け検査データの正規化技術の開発
BoxCox変換 トランケーション
新
旧
新旧測定法同時測定データ
回帰係数
変換係数
旧測定法1年毎の集団データ
検証用実験データ
長期の検査データ
検証
一般的な検査室では、この実験データは無いし、過去の資料の記録が残っている施設は少ない
測定法の切り替え時に、新旧の測定法を使って数百件のサンプルを同時に分析する
集団の平均
目的：蓄積された膨大なデータの分布から、変換のための係数を求める
ゴールドスタンダード
旧測定法から新測定法への変換係数
28
0
50
100
150
200
250
300
350
400平均 変換
正規化処理結果
LD(LDH)
年月
新しい測定法の基準に正規化
分析精度を考慮する必要があるが正規化されたデータは、長期時系列変動をシームレスに解析可能
測定方法の変更
29
0
5
10
15
20
25
30
平均 変換
AST(GOT)では季節内変動のため要注意
AST(GOT)
年月
夏低く、冬は高い
測定方法の変更
30
トランケーション平均法適用の注意点
• 対象の束縛要
– 性差，年齢差で分布が変化
• 季節内変動がある検査項目
– AST, ALT, ALP, γGT, ChE, T‐Bilなど
202122232425262728
01 02 03 04 05 06 07 08 09 10 11 12
AST
月
春夏低く、冬は高い
（40～60歳男性）
検査項目により性差、年齢差あり
32
集団の偏りによる誤差に配慮要
GLUの性差、年齢差
33
CREの性差
34
分布パターン情報からデータの標準化を可能とする
20 30 40 50 60 70 80 90
4
4.4
4.8
5.2
5.6
6
6.4
6.8
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Age
HbA1c
ある年齢層のデータを取得し、その分布パターンから、補正係数を求め
20 30 40 50 60 70 80 90
4
4.4
4.8
5.2
5.6
6
6.4
6.8
年齢基準値分布パターン標準分布パターン
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ある施設の特定年齢性別データ
40～50才
データの分布パターンから標準分布パターンへの変換係数を求める
未知の施設データであっても
その、一部のパターンから、検査値の標準化と各年齢層の基準値の推定が可能
未知のレンジ
まとめ
• 検査精度の管理
– 精度、正確性
• 蓄積データに基づく誤差補正
– 施設間補正
– 他の影響：性差、年齢差、測定季節
– システム構築
